钆塞酸二钠增强磁共振图像纹理分析对于评价大鼠肝纤维化的价值（英文）
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摘要
目的对钆塞酸二钠增强磁共振T1 mapping成像以及T1加权成像(T1W)、T2加权成像(T2W)、表观扩散系数(ADC)图像进行纹理分析,探讨其在大鼠模型中定量评价肝纤维化的价值。方法采用四氯化碳腹腔注射法构建大鼠肝纤维化模型,打药时长4～12周(n=30),在对照组中(n=10)注射等量生理盐水。磁共振扫描序列包括T2W,弥散加权成像,注射对比剂前后的T1W和T1 mapping系列图像。利用METAVIR分级将肝纤维化分为正常(F0),轻度肝纤维化(F1～2)和重度肝纤维化(F3～4)。纹理特征参数包括平均灰度强度,标准差,熵,正象素均值、偏斜和峰度。采用非参数Mann-Whitney U检验比较各个序列的上述各项纹理参数在F0与F≥1间、以及F0～2与F3～4间的差异。利用受试者工作特征曲线(ROC)分析纹理参数在区分正常肝组织和肝纤维化、F0～2和F3～4的诊断准确性,并获得ROC曲线下面积(AUC)以评价纹理参数的诊断价值。结果 20只大鼠完成磁共振T1 mapping扫描,其病理结果为正常(F0,n=6),轻度肝纤维化(F1～2,n=5)和重度肝纤维化(F3～4,n=9)。在打药前T1 mapping图像中,在各个空间尺度因子(spatial scaling factor,SSF)下,F≥1组的熵值均显著大于F0组(P=0.015,0.015,0.015,0.013,0.015,0.018,分别对应于SSF=0,2,3,4,5,6);在SSF 4,5,6下,F≥1组的平均灰度强度显著高于F0组(P=0.004,0.006,0.013)。在区分正常和肝纤维化组时,打药前T1 mapping图像的熵和平均灰度强度在大多数SSF下,展现出中等诊断价值。结论部分钆塞酸二钠增强磁共振图像的纹理参数,特别是打药前T1 mapping图像的熵,对于肝纤维化的诊断具有一定价值。
Objective To explore the ability of texture analysis of gadoxetic acid-enhanced magnetic resonance imaging(MRI) T1 mapping images, as well as T1-weighted(T1 W), T2-weighted(T2 W) and apparent diffusion coefficient(ADC) maps for distinguishing between varying degrees of hepatic fibrosis in an experimental rat model.Methods Liver fibrosis in rats was induced by carbon tetrachloride intraperitoneal injection for 4–12 weeks(n = 30). In the control group(n = 10) normal saline was applied. The MRI protocol contained T2 W, diffusion weighted imaging, pre-and post-contrast image series of T1 W and T1 mapping images. METAVIR score was used to grade liver fibrosis as normal(F0), mild fibrosis(F1–2), and advanced fibrosis(F3–4). Texture parameters including mean gray-level intensity(Mean), standard deviation(SD), Entropy, mean of positive pixels(MPP), Skewness, and Kurtosis were obtained. Nonparametric Mann-Whitney U test was used to compare the average value of each texture parameter in each sequence for assessing the difference between F0 and F≥1 as well as F0–2 and F3–4. Receiver operating characteristic(ROC) curves were obtained to assess the diagnosing accuracy of the parameters for differentiating no liver fibrosis from liver fibrosis and rats with liver fibrosis grading F0–2 from those with grading F3–4. The area under ROC curve(AUC) was calculated to evaluate the diagnostic efficiency of texture parameters.Results Finally, 20 rats completed MR T1 mapping image scan. The pathologic staging of these 20 rats was no fibrosis(F0, n = 6), mild fibrosis(F1–2, n = 5) and advanced fibrosis(F3–4, n = 9). On pre-contrast T1 mapping image, Entropy was seen to be statistically significant higher in the F≥1 group than that in the F0 group at each spatial scaling factor(SSF) setting(P = 0.015, 0.015, 0.015, 0.013, 0.015 and 0.018 respectively to SSF = 0, 2, 3, 4,5, 6), and Mean of the F≥1 rats was statistically significant higher than that of the F0 rats at SSF 4, 5, 6(P = 0.004, 0.006, and 0.013, respectively). Entropy and Mean showed a moderate diagnostic performance in most SSF settings of T1 mapping pre-contrast images for differentiation of normal liver from liver fibrosis.Conclusion Certain texture features of gadoxetic acid-enhanced MR images, especially the Entropy of noncontrast T1 mapping image, was found to be a useful biomarker for the diagnosis of liver fibrosis.
Objective To explore the ability of texture analysis of gadoxetic acid-enhanced magnetic resonance imaging(MRI) T1 mapping images, as well as T1-weighted(T1 W), T2-weighted(T2 W) and apparent diffusion coefficient(ADC) maps for distinguishing between varying degrees of hepatic fibrosis in an experimental rat model.Methods Liver fibrosis in rats was induced by carbon tetrachloride intraperitoneal injection for 4–12 weeks(n = 30). In the control group(n = 10) normal saline was applied. The MRI protocol contained T2 W, diffusion weighted imaging, pre-and post-contrast image series of T1 W and T1 mapping images. METAVIR score was used to grade liver fibrosis as normal(F0), mild fibrosis(F1–2), and advanced fibrosis(F3–4). Texture parameters including mean gray-level intensity(Mean), standard deviation(SD), Entropy, mean of positive pixels(MPP), Skewness, and Kurtosis were obtained. Nonparametric Mann-Whitney U test was used to compare the average value of each texture parameter in each sequence for assessing the difference between F0 and F≥1 as well as F0–2 and F3–4. Receiver operating characteristic(ROC) curves were obtained to assess the diagnosing accuracy of the parameters for differentiating no liver fibrosis from liver fibrosis and rats with liver fibrosis grading F0–2 from those with grading F3–4. The area under ROC curve(AUC) was calculated to evaluate the diagnostic efficiency of texture parameters.Results Finally, 20 rats completed MR T1 mapping image scan. The pathologic staging of these 20 rats was no fibrosis(F0, n = 6), mild fibrosis(F1–2, n = 5) and advanced fibrosis(F3–4, n = 9). On pre-contrast T1 mapping image, Entropy was seen to be statistically significant higher in the F≥1 group than that in the F0 group at each spatial scaling factor(SSF) setting(P = 0.015, 0.015, 0.015, 0.013, 0.015 and 0.018 respectively to SSF = 0, 2, 3, 4,5, 6), and Mean of the F≥1 rats was statistically significant higher than that of the F0 rats at SSF 4, 5, 6(P = 0.004, 0.006, and 0.013, respectively). Entropy and Mean showed a moderate diagnostic performance in most SSF settings of T1 mapping pre-contrast images for differentiation of normal liver from liver fibrosis.Conclusion Certain texture features of gadoxetic acid-enhanced MR images, especially the Entropy of noncontrast T1 mapping image, was found to be a useful biomarker for the diagnosis of liver fibrosis.
引文
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7.Jirák D,DezortováM,Taimr P,et al.Texture analysis of human liver.J Magn Reson Imaging 2002;15(1):68-74.
8.Katsube T,Okada M,Kumano S,et al.Estimation of liver function using T1 mapping on Gd-EOB-DTPA-enhanced magnetic resonance imaging.Invest Radiol 2011;46(4):277-83.doi:10.1097/RLI.0b013e318200f67d.
9.Zhou ZP,Long LL,Huang LJ,et al.Gd-EOB-DTPA-enhanced MRI T1 mapping for assessment of liver function in rabbit fibrosis model:comparison of hepatobiliary phase images obtained at 10 and 20 min.Radiol Med 2017;122(4):239-47.doi:10.1007/s11547-016-0719-1.
10.Zhou ZP,Long LL,Qiu WJ,et al.Comparison of 10-and 20-min hepatobiliary phase images on Gd-EOB-DTPA-enhanced MRI T1 mapping for liver function assessment in clinic.Abdom Radiol(NY)2017;42(9):2272-8.doi:10.1007/s00261-017-1143-2.
11.Ding Y,Rao SX,Zhu T,et al.Liver fibrosis staging using T1 mapping on gadoxetic acid-enhanced MRI compared with DW imaging.Clin Radiol 2015;70(10):1096-103.doi:10.1016/j.crad.2015.04.014.
12.Pan S,Wang XQ,Guo QY.Quantitative assessment of hepatic fibrosis in chronic hepatitis B and C:T1mapping on Gd-EOB-DTPA-enhanced liver magnetic resonance imaging.WJG 2018;24(18):2024-35.doi:10.3748/wjg.v24.i18.2024.
13.Intraobserver and interobserver variations in liver biopsy interpretation in patients with chronic hepatitis C.The French METAVIR Cooperative Study Group.Hepatology 1994;20(1 Pt 1):15-20.
14.Ganeshan B,Miles KA.Quantifying tumour heterogeneity with CT.Cancer Imaging 2013;13(1):140-9.doi:10.1102/1470-7330.2013.0015.
15.Lubner MG,Malecki K,Kloke J,et al.Texture analysis of the liver at MDCT for assessing hepatic fibrosis.Abdom Radiol(NY)2017;42(8):2069-78.doi:10.1007/s00261-017-1096-5.
16.Raman SP,Schroeder JL,Huang P,et al.Preliminary data using computed tomography texture analysis for the classification of hypervascular liver lesions:generation of a predictive model on the basis of quantitative spatial frequency measurements-a work in progress.J Comput Assist Tomogr 2015;39(3):383-95.doi:10.1097/RCT.0000000000000217.
17.Cannella R,Rangaswamy B,Minervini MI,et al.Value of texture analysis on gadoxetic acid-enhanced MRIfor differentiating hepatocellular adenoma from focal nodular hyperplasia.AJR Am J Roentgenol 2019;212(3):538-46.doi:10.2214/AJR.18.20182.
18.MuléS,Thiefin G,Costentin C,et al.Advanced hepatocellular carcinoma:pretreatment contrast-enhanced CT texture parameters as predictive biomarkers of survival in patients treated with sorafenib.Radiology 2018;288(2):445-55.doi:10.1148/radiol.2018171320.
19.Chen S,Zhu Y,Liu Z,et al.Texture analysis of baseline multiphasic hepatic computed tomography images for the prognosis of single hepatocellular carcinoma after hepatectomy:a retrospective pilot study.Eur J Radiol 2017;90:198-204.doi:10.1016/j.ejrad.2017.02.035.
20.Canellas R,Mehrkhani F,Patino M,et al.Characterization of portal vein thrombosis(neoplastic versus bland)on CT images using software-based texture analysis and thrombus density(Hounsfield Units).AJRAm J Roentgenol 2016;207(5):W81-7.doi:10.2214/AJR.15.15928.
21.Sadot E,Simpson AL,Do RKG,et al.Cholangiocarcinoma:correlation between molecular profiling and imaging phenotypes.PLoS One 2015;10(7):e0132953.doi:10.1371/journal.pone.0132953.
22.Daginawala N,Li B,Buch K,et al.Using texture analyses of contrast enhanced CT to assess hepatic fibrosis.Eur J Radiol 2016;85(3):511-7.doi:10.1016/j.ejrad.2015.12.009.
23.Romero-Gómez M,Gómez-González E,Madrazo A,et al.Optical analysis of computed tomography images of the liver predicts fibrosis stage and distribution in chronic hepatitis C.Hepatology 2008;47(3):810-6.doi:10.1002/hep.22112.
24.Zhang X,Gao X,Liu BJ,et al.Effective staging of fibrosis by the selected texture features of liver:which one is better,CT or MR imaging?Comput Med Imaging Graph 2015;46 Pt 2:227-36.doi:10.1016/j.com pmedimag.2015.09.003.
25.Jirák D,DezortováM,Taimr P,et al.Texture analysis of human liver.J Magn Reson Imaging 2002;15(1):68-74.
26.Lubner MG,Stabo N,Lubner SJ,et al.CT textural analysis of hepatic metastatic colorectal cancer:pre-treatment tumor heterogeneity correlates with pathology and clinical outcomes.Abdom Imaging2015;40(7):2331-7.doi:10.1007/s00261-015-0438-4.
27.Ng F,Kozarski R,Ganeshan B,et al.Assessment of tumor heterogeneity by CT texture analysis:can the largest cross-sectional area be used as an alternative to whole tumor analysis?Eur J Radiol 2013;82(2):342-8.doi:10.1016/j.ejrad.2012.10.023.
28.Yoon JH,Lee JM,Paek M,et al.Quantitative assessment of hepatic function:modified look-locker inversion recovery(MOLLI)sequence for T1 mapping on Gd-EOB-DTPA-enhanced liver MR imaging.Eur Radiol2016;26(6):1775-82.doi:10.1007/s00330-015-3994-7.
29.Yoon JH,Lee JM,Kang H-J,et al.Quantitative assessment of liver function by using gadoxetic acid-enhanced MRI:hepatocyte uptake ratio.Radiology 2019;290(1):125-33.doi:10.1148/radiol.2018180753.
2.Nguyen D,Talwalkar JA.Noninvasive assessment of liver fibrosis.Hepatology 2011;53(6):2107-10.doi:10.1002/hep.24401.
3.Lubner MG,Smith AD,Sandrasegaran K,et al.CTtexture analysis:definitions,applications,biologic correlates,and challenges.RadioGraphics 2017;37(5):1483-503.doi:10.1148/rg.2017170056.
4.Fujimoto K,Tonan T,Azuma S,et al.Evaluation of the mean and entropy of apparent diffusion coefficient values in chronic hepatitis C:correlation with pathologic fibrosis stage and inflammatory activity grade.Radiology 2011;258(3):739-48.doi:10.1148/radiol.10100853.
5.Barry B,Buch K,Soto JA,et al.Quantifying liver fibrosis through the application of texture analysis to diffusion weighted imaging.Magn Reson Imaging2014;32(1):84-90.doi:10.1016/j.mri.2013.04.006.
6.Bahl G,Cruite I,Wolfson T,et al.Noninvasive classification of hepatic fibrosis based on texture parameters from double contrast-enhanced magnetic resonance images.J Magn Reson Imaging 2012;36(5):1154-61.doi:10.1002/jmri.23759.
7.Jirák D,DezortováM,Taimr P,et al.Texture analysis of human liver.J Magn Reson Imaging 2002;15(1):68-74.
8.Katsube T,Okada M,Kumano S,et al.Estimation of liver function using T1 mapping on Gd-EOB-DTPA-enhanced magnetic resonance imaging.Invest Radiol 2011;46(4):277-83.doi:10.1097/RLI.0b013e318200f67d.
9.Zhou ZP,Long LL,Huang LJ,et al.Gd-EOB-DTPA-enhanced MRI T1 mapping for assessment of liver function in rabbit fibrosis model:comparison of hepatobiliary phase images obtained at 10 and 20 min.Radiol Med 2017;122(4):239-47.doi:10.1007/s11547-016-0719-1.
10.Zhou ZP,Long LL,Qiu WJ,et al.Comparison of 10-and 20-min hepatobiliary phase images on Gd-EOB-DTPA-enhanced MRI T1 mapping for liver function assessment in clinic.Abdom Radiol(NY)2017;42(9):2272-8.doi:10.1007/s00261-017-1143-2.
11.Ding Y,Rao SX,Zhu T,et al.Liver fibrosis staging using T1 mapping on gadoxetic acid-enhanced MRI compared with DW imaging.Clin Radiol 2015;70(10):1096-103.doi:10.1016/j.crad.2015.04.014.
12.Pan S,Wang XQ,Guo QY.Quantitative assessment of hepatic fibrosis in chronic hepatitis B and C:T1mapping on Gd-EOB-DTPA-enhanced liver magnetic resonance imaging.WJG 2018;24(18):2024-35.doi:10.3748/wjg.v24.i18.2024.
13.Intraobserver and interobserver variations in liver biopsy interpretation in patients with chronic hepatitis C.The French METAVIR Cooperative Study Group.Hepatology 1994;20(1 Pt 1):15-20.
14.Ganeshan B,Miles KA.Quantifying tumour heterogeneity with CT.Cancer Imaging 2013;13(1):140-9.doi:10.1102/1470-7330.2013.0015.
15.Lubner MG,Malecki K,Kloke J,et al.Texture analysis of the liver at MDCT for assessing hepatic fibrosis.Abdom Radiol(NY)2017;42(8):2069-78.doi:10.1007/s00261-017-1096-5.
16.Raman SP,Schroeder JL,Huang P,et al.Preliminary data using computed tomography texture analysis for the classification of hypervascular liver lesions:generation of a predictive model on the basis of quantitative spatial frequency measurements-a work in progress.J Comput Assist Tomogr 2015;39(3):383-95.doi:10.1097/RCT.0000000000000217.
17.Cannella R,Rangaswamy B,Minervini MI,et al.Value of texture analysis on gadoxetic acid-enhanced MRIfor differentiating hepatocellular adenoma from focal nodular hyperplasia.AJR Am J Roentgenol 2019;212(3):538-46.doi:10.2214/AJR.18.20182.
18.MuléS,Thiefin G,Costentin C,et al.Advanced hepatocellular carcinoma:pretreatment contrast-enhanced CT texture parameters as predictive biomarkers of survival in patients treated with sorafenib.Radiology 2018;288(2):445-55.doi:10.1148/radiol.2018171320.
19.Chen S,Zhu Y,Liu Z,et al.Texture analysis of baseline multiphasic hepatic computed tomography images for the prognosis of single hepatocellular carcinoma after hepatectomy:a retrospective pilot study.Eur J Radiol 2017;90:198-204.doi:10.1016/j.ejrad.2017.02.035.
20.Canellas R,Mehrkhani F,Patino M,et al.Characterization of portal vein thrombosis(neoplastic versus bland)on CT images using software-based texture analysis and thrombus density(Hounsfield Units).AJRAm J Roentgenol 2016;207(5):W81-7.doi:10.2214/AJR.15.15928.
21.Sadot E,Simpson AL,Do RKG,et al.Cholangiocarcinoma:correlation between molecular profiling and imaging phenotypes.PLoS One 2015;10(7):e0132953.doi:10.1371/journal.pone.0132953.
22.Daginawala N,Li B,Buch K,et al.Using texture analyses of contrast enhanced CT to assess hepatic fibrosis.Eur J Radiol 2016;85(3):511-7.doi:10.1016/j.ejrad.2015.12.009.
23.Romero-Gómez M,Gómez-González E,Madrazo A,et al.Optical analysis of computed tomography images of the liver predicts fibrosis stage and distribution in chronic hepatitis C.Hepatology 2008;47(3):810-6.doi:10.1002/hep.22112.
24.Zhang X,Gao X,Liu BJ,et al.Effective staging of fibrosis by the selected texture features of liver:which one is better,CT or MR imaging?Comput Med Imaging Graph 2015;46 Pt 2:227-36.doi:10.1016/j.com pmedimag.2015.09.003.
25.Jirák D,DezortováM,Taimr P,et al.Texture analysis of human liver.J Magn Reson Imaging 2002;15(1):68-74.
26.Lubner MG,Stabo N,Lubner SJ,et al.CT textural analysis of hepatic metastatic colorectal cancer:pre-treatment tumor heterogeneity correlates with pathology and clinical outcomes.Abdom Imaging2015;40(7):2331-7.doi:10.1007/s00261-015-0438-4.
27.Ng F,Kozarski R,Ganeshan B,et al.Assessment of tumor heterogeneity by CT texture analysis:can the largest cross-sectional area be used as an alternative to whole tumor analysis?Eur J Radiol 2013;82(2):342-8.doi:10.1016/j.ejrad.2012.10.023.
28.Yoon JH,Lee JM,Paek M,et al.Quantitative assessment of hepatic function:modified look-locker inversion recovery(MOLLI)sequence for T1 mapping on Gd-EOB-DTPA-enhanced liver MR imaging.Eur Radiol2016;26(6):1775-82.doi:10.1007/s00330-015-3994-7.
29.Yoon JH,Lee JM,Kang H-J,et al.Quantitative assessment of liver function by using gadoxetic acid-enhanced MRI:hepatocyte uptake ratio.Radiology 2019;290(1):125-33.doi:10.1148/radiol.2018180753.